Package: BioM2
Title: Biologically Explainable Machine Learning Framework
Version: 1.1.3
Authors@R: c(
     person(given = "Shunjie",
                   family = "Zhang",
                   role = c("aut", "cre"),
                   email = "zhang.shunjie@qq.com"),
      person(given = "Junfang",
                    family = "Chen",
                    role = c("aut")))
Description: Biologically Explainable Machine Learning Framework for Phenotype Prediction using omics data described in Chen and Schwarz (2017) <doi:10.48550/arXiv.1712.00336>.Identifying reproducible and interpretable biological patterns from high-dimensional omics data is a critical factor in understanding the risk mechanism of complex disease. As such, explainable machine learning can offer biological insight in addition to personalized risk scoring.In this process, a feature space of biological pathways will be generated, and the feature space can also be subsequently analyzed using WGCNA (Described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559> ) methods.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: WGCNA, mlr3, CMplot, ggsci, ROCR, caret, ggplot2, ggpubr,
        viridis, ggthemes, ggstatsplot, htmlwidgets, mlr3verse,
        parallel, uwot, webshot, wordcloud2,ggforce, igraph, ggnetwork
Depends: R (>= 4.1.0)
LazyData: true
NeedsCompilation: no
Packaged: 2025-07-17 08:11:25 UTC; Shedom
Author: Shunjie Zhang [aut, cre],
  Junfang Chen [aut]
Maintainer: Shunjie Zhang <zhang.shunjie@qq.com>
Repository: CRAN
Date/Publication: 2025-07-17 08:30:02 UTC
Built: R 4.6.0; ; 2025-11-13 05:55:27 UTC; windows
